my team and i are very happy to share news of our very first publication in the field of the Science of Learning, with specific reference to neuroscience :-) our paper is published in a special issue of Sustainability, themed on 'Aspirations within Interdisciplinary STEM/STEAM Education under the Education for Sustainable Development (ESD)'.
since April 2021, we have been working hard on Learning at the intersection of AI, physiology, EEG, our environment and well-being (the Life2Well Project), in which we aim to afford learners opportunities to learn about their own learning, while they are learning, from a citizen science perspective.
an important part of our work is the validation of our maker-centric electroencephalographic headsets against industrial grade headsets.
with the help of Dr Yuvaraj Rajamanickam and Dr Jack Fogarty of the Science of Learning in Education Centre at the National Institute of Education, we report our work in Volume 14 Issue 17 of the journal, as Investigating the Effects of Microclimate on Physiological Stress and Brain Function with Data Science and Wearables.
the abstract of our paper reads:
This paper reports a study conducted by students as an independent research project under the mentorship of a research scientist at the National Institute of Education, Singapore. The aim of the study was to explore the relationships between local environmental stressors and physiological responses from the perspective of citizen science. Starting from July 2021, data from EEG headsets were complemented by those obtained from smartwatches (namely heart rate and its variability and body temperature and stress score). Identical units of a wearable device containing environmental sensors (such as ambient temperature, air pressure, infrared radiation, and relative humidity) were designed and worn, respectively, by five adolescents for the same period. More than 100,000 data points of different types—neurological, physiological, and environmental—were eventually collected and were processed through a random forest regression model and deep learning models. The results showed that the most influential microclimatic factors on the biometric indicators were noise and the concentrations of carbon dioxide and dust. Subsequently, more complex inferences were made from the Shapley value interpretation of the regression models. Such findings suggest implications for the design of living conditions with respect to the interaction of the microclimate and human health and comfort.